Ranking Conservation and Timber Management Options for Leadbeater’s Possum in Southeastern Australia Using Population Viability Analysis

Lindenmayer, David B.; Possingham, Hugh P.

doi:10.1046/j.1523-1739.1996.10010235.x

Cited by 135 publications

(91 citation statements)

References 24 publications

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“…By including habitat dynamics in spatially explicit metapopulation models (Fahrig, 1992;Gyllenberg and Hanski, 1997;Brachet et al, 1999;Johnson, 2000;Keymer et al, 2000;Amarasekare and Possingham, 2001;Ellner and Fussmann, 2003;Thomas and Hanski, 2004), it is possible to provide management and conservation plans for species in successional systems and systems subject to occasional disturbance (Possingham et al, 1994;Lindenmayer and Possingham, 1996;McCarthy and Lindenmayer, 2000;Amarasekare and Possingham, 2001;Akcakaya et al, 2004). In this paper, we use a metapopulation and patch dynamic simulation model using object-oriented programming to look closely at the implications of spatial and temporal structure of disturbances in metapopulations with different spatial patch configurations.…”

Section: Discussionmentioning

confidence: 99%

How patch configuration affects the impact of disturbances on metapopulation persistence

Vuilleumier

Wilcox

Cairns

et al. 2007

Theoretical Population Biology

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Disturbances affect metapopulations directly through reductions in population size and indirectly through habitat modification. We consider how metapopulation persistence is affected by different disturbance regimes and the way in which disturbances spread, when metapopulations are compact or elongated, using a stochastic spatially explicit model which includes metapopulation and habitat dynamics. We discover that the risk of population extinction is larger for spatially aggregated disturbances than for spatially random disturbances. By changing the spatial configuration of the patches in the system-leading to different proportions of edge and interior patches-we demonstrate that the probability of metapopulation extinction is smaller when the metapopulation is more compact. Both of these results become more pronounced when colonization connectivity decreases. Our results have important management implication as edge patches, which are invariably considered to be less important, may play an important role as disturbance refugia. r

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Section: Discussionmentioning

confidence: 99%

How patch configuration affects the impact of disturbances on metapopulation persistence

Vuilleumier

Wilcox

Cairns

et al. 2007

Theoretical Population Biology

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“…More recent applications, such as those employing Marxan in real-life planning situations, have a threshold amount of the species distribution that they strive to include in the system; the Marxan optimization function is set up so that little value is obtained below the threshold, and little additional value is obtained for going over the threshold (Ball et al 2009). The ultimate measure of conservation value would be a quantitative model of extinction risk; a reasonable proxy (that avoids complex dynamic modeling) might be an increasing function of the fraction of the species distribution or suitable habitat included in the system (e.g., Lindenmayer and Possingham 1996). In the work that follows we use the threshold criterion as implemented in Marxan, but we expect that many of the qualitative results will apply to other value functions as well.…”

Section: Mathematical Analysismentioning

confidence: 99%

The role of scale in designing protected area systems to conserve poorly known species

2015

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Possingham. 2015. The role of scale in designing protected area systems to conserve poorly known species. Ecosphere 6(11):237. http://dx.doi.org/10.1890/ES15-00346.1Abstract. Systematic conservation planning has a substantial theoretical underpinning that allows optimization of tradeoffs between biodiversity conservation and other socioeconomic goals. However, this theory assumes perfect spatial information about the locations of biodiversity features (e.g., species distributions). In practice, planners represent well-known taxa and other biodiversity ''surrogates'' in protected area systems, hoping that unmapped species will also be conserved. However, empirical research finds that surrogates predict species presence imperfectly, and sometimes rather poorly, at scales relevant to planning, and existing theory provides no further guidance. We developed new theory, explicitly incorporating aspects of spatial scale, for the representation problem when the locations of species distributions are unknown. Using probability theory and simulated and real species distributions, we found that the probability of adequately representing an unmapped species in a protected area system will be low unless the total fraction of the region being protected is larger than the species representation target. Furthermore, successful conservation depended critically on the relative sizes of the species distribution and of the individual protected areas; fewer, larger protected areas allowed the entire species distribution to fall into an unprotected gap. This scale-dependence varied with the configuration of the protected area system, with the conservation objective most likely to be attained if the individual protected areas were hyperdispersed (evenly spaced across the planning region). Using these results, we developed three design principles for representing unmapped species in protected areas: (1) The fraction of the region placed in protected areas should be substantially larger than the species-level representation target; (2) Individual protected areas must be at least one to two orders of magnitude smaller than the unmapped species' distribution; and (3) Protected areas should be evenly dispersed over geographic space. We also performed preliminary investigations of the effects of surrogates and socio-economic cost data on the probability of adequately representing unmapped species, finding that the primary effect of surrogates may simply be to promote hyperdispersion of protected areas across the planning region, and that seeking to minimize opportunity costs gives poorer conservation results than random protected area placement.

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“…In practice, there has been a range of alternative uses. PVAs can be used to: (1) predict the future size of a population 1,5,6 ; (2) estimate the probability of a population going extinct over a given time 5 ; (3) assess which of a suite of management or conservation strategies is likely to maximize the probability of a population persisting 7 ; and (4) explore the consequences of different assumptions on population dynamics for small populations 8 . In reality, only the predictive accuracy of the first two cases is estimable, as there are rarely sufficient replicate populations from which to collect data to determine whether the comparative predictions of the third use are accurate, and the fourth use has not generated testable predictions.…”

Section: Do Pvas Work?mentioning

confidence: 99%

The use and abuse of population viability analysis

Coulson

Mace

Hudson

et al. 2001

Trends in Ecology & Evolution

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A recent study by Brook et al. empirically tested the performance of population viability analysis (PVA) using data from 21 populations across a wide range of species. The study concluded that PVAs are good at predicting the future dynamics of populations. We suggest that this conclusion is a result of a bias in the studies that Brook et al. included in their analyses. We present arguments that PVAs can only be accurate at predicting extinction probabilities if data are extensive and reliable, and if the distribution of vital rates between individuals and years can be assumed stationary in the future, or if any changes can be accurately predicted. In particular, we note that although catastrophes are likely to have precipitated many extinctions, estimates of the probability of catastrophes are unreliable.Population viability analysis (PVA) is a modelling tool that estimates the future size and risk of extinction for populations of organisms 1,2 . PVA works by using life-history or population growth-rate data to parameterize a population model that is then used to project dynamics and estimate future population size and structure 3 . User-friendly PVA software packages allow conservation managers to predict future population sizes and risks of extinction for any population they choose 3 . Because of this ease of application of PVAs, it is important to determine and understand the limits to their predictive accuracy 1,4-6 . Brook et al. have tested the predictive accuracy of PVA using data from many populations and conclude that PVA is not a useless tool, and that it should not be dispensed with in favour of alternative untested methods. Do PVAs work?The predictive accuracy of a PVA will depend on the purpose to which it is being applied. In practice, there has been a range of alternative uses. PVAs can be used to: (1) predict the future size of a population 1,5,6 ; (2) estimate the probability of a population going extinct over a given time 5 ; (3) assess which of a suite of management or conservation strategies is likely to maximize the probability of a population persisting 7 ; and (4) explore the consequences of different assumptions on population dynamics for small populations 8 . In reality, only the predictive accuracy of the first two cases is estimable, as there are rarely sufficient replicate populations from which to collect data to determine whether the comparative predictions of the third use are accurate, and the fourth use has not generated testable predictions.There are two ways that the predictive accuracy of PVAs can be assessed. The first approach is to use historical data and, at a point in the future, predict the population size and compare this to what actually happened. To avoid circularity, the data used to parameterize the model should not include data from the time-period over which predictions are made. The whole population exists as a suite of spatially structured local populations) can then be compared to the distribution of predicted population sizes from the model projections...

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Ranking Conservation and Timber Management Options for Leadbeater’s Possum in Southeastern Australia Using Population Viability Analysis

Cited by 135 publications

References 24 publications

How patch configuration affects the impact of disturbances on metapopulation persistence

How patch configuration affects the impact of disturbances on metapopulation persistence

The role of scale in designing protected area systems to conserve poorly known species

The use and abuse of population viability analysis

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